{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"ResNet.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"cjDKaAphtQQN","colab_type":"code","colab":{}},"source":["import torch\n","import torchvision\n","import numpy as np \n","import random\n","import matplotlib.pyplot as plt\n","import time"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oIGpmBmFRKjr","colab_type":"code","colab":{}},"source":["random.seed(0)\n","np.random.seed(0)\n","torch.manual_seed(0)\n","torch.cuda.manual_seed(0)\n","torch.backends.cudnn.deterministic=True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Pnu5Wh-Zv1LX","colab_type":"code","outputId":"f733d2f5-018a-47b4-82f9-a67fa0723654","executionInfo":{"status":"ok","timestamp":1571140608575,"user_tz":-180,"elapsed":2322,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["CIFAR_train = torchvision.datasets.CIFAR10('/', download=True, train=True)\n","CIFAR_test = torchvision.datasets.CIFAR10('/', download=True, train=False)"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Files already downloaded and verified\n","Files already downloaded and verified\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"NTgS_ntewX7W","colab_type":"code","outputId":"06b4979b-16e7-4a5e-9c26-852b482041a9","executionInfo":{"status":"ok","timestamp":1571140608869,"user_tz":-180,"elapsed":2400,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["type(CIFAR_train.data)"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"8ZGX0tUGwMBe","colab_type":"code","colab":{}},"source":["X_train = torch.FloatTensor(CIFAR_train.data)\n","y_train = torch.LongTensor(CIFAR_train.targets)\n","\n","X_test = torch.FloatTensor(CIFAR_test.data)\n","y_test = torch.LongTensor(CIFAR_test.targets)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"w_fGo1SCw85m","colab_type":"code","outputId":"c412f831-cc61-45a5-ee8f-dd7db274febe","executionInfo":{"status":"ok","timestamp":1571140609753,"user_tz":-180,"elapsed":2398,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, X_test.shape"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([50000, 32, 32, 3]), torch.Size([10000, 32, 32, 3]))"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"bjhKz3iBxEg2","colab_type":"code","outputId":"ac0ee9e7-135b-4cca-c9fd-ad2b56c07fbd","executionInfo":{"status":"ok","timestamp":1571140609950,"user_tz":-180,"elapsed":1756,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.min(), X_train.max()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensor(0.), tensor(255.))"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"gemhoO1jxHzD","colab_type":"code","colab":{}},"source":["X_train /= 255.\n","X_test /= 255."],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"FT9rU__kyyUC","colab_type":"code","outputId":"2e4f581f-fe49-4bd9-c833-17657c61d101","executionInfo":{"status":"ok","timestamp":1571140609952,"user_tz":-180,"elapsed":692,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["CIFAR_train.classes"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['airplane',\n"," 'automobile',\n"," 'bird',\n"," 'cat',\n"," 'deer',\n"," 'dog',\n"," 'frog',\n"," 'horse',\n"," 'ship',\n"," 'truck']"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"OOnC3dNJyyCK","colab_type":"code","outputId":"89f44d06-df15-4c8e-a859-938f711b65ac","executionInfo":{"status":"ok","timestamp":1571140613992,"user_tz":-180,"elapsed":4507,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["for i in range(20):\n","  plt.figure(figsize=(3, 3))\n","  plt.imshow(X_train[i])\n","  plt.title(CIFAR_train.classes[y_train[i]])"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAMgAAADSCAYAAAAPFY9jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRcJ2t71sho33gRSnZ/R3s4GXm2cjfXQT11+M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LMtXHcvy/AXByn6fBIuuOE4AriOCE0NN0dECgqDcLpKTaeMzNG+/thXhw+YURRAaDV6B547VWb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\n44TgCuI4IbiCOE4IriCOE4IriOOE8P/oNr6h/OlNmAAAAABJRU5ErkJggg==\n","text/plain":["<Figure 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qQnhTXk5TB9SBhwAZ+boGXkahW+CPPL7MTAgB0gM8nnuH9F5bYqJ5b5tSXHUbnjLJRk2OlH5XK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\nRU5ErkJggg==\n","text/plain":["<Figure size 216x216 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"SL7KPPjVzbMr","colab_type":"code","outputId":"3ab65050-f06e-4220-ea38-940e6596c20c","executionInfo":{"status":"ok","timestamp":1571140613995,"user_tz":-180,"elapsed":4341,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, X_test.shape"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([50000, 32, 32, 3]), torch.Size([10000, 32, 32, 3]))"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"JvS1LNTUxRYH","colab_type":"code","colab":{}},"source":["X_train = X_train.permute(0, 3, 1, 2)\n","X_test = X_test.permute(0, 3, 1, 2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Oub2FOV7zvy2","colab_type":"code","outputId":"2770469e-1d85-41d6-df5b-4bd6614bea5a","executionInfo":{"status":"ok","timestamp":1571140613997,"user_tz":-180,"elapsed":3987,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, X_test.shape"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([50000, 3, 32, 32]), torch.Size([10000, 3, 32, 32]))"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"DkyluNUHhBMc","colab_type":"code","colab":{}},"source":["class LambdaLayer(torch.nn.Module):\n","  def __init__(self, lambd):\n","    super().__init__()\n","    self.lambd = lambd\n","    \n","  def forward(self, x):\n","    return self.lambd(x)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cd712y_7vE2K","colab_type":"code","colab":{}},"source":["class BasicBlock(torch.nn.Module):\n","  expansion = 1\n","  \n","  def __init__(self, \n","               in_channels, \n","               out_channels, \n","               stride=1, \n","               use_batch_norm=True,\n","               use_dropout=False,\n","               dropout_prob=0.2,\n","               option='A'):\n","    super().__init__()\n","    self.use_batch_norm = use_batch_norm\n","    self.use_dropout = use_dropout\n","    self.dropout_prob = dropout_prob\n","    \n","    self.conv1 = torch.nn.Conv2d(in_channels, out_channels, \n","                                 kernel_size=3, stride=stride,\n","                                 padding=1, bias=False)\n","    self.bn1 = torch.nn.BatchNorm2d(out_channels)\n","    self.dropout1 = torch.nn.Dropout2d(self.dropout_prob)\n","    \n","    self.conv2 = torch.nn.Conv2d(out_channels, out_channels,\n","                                 kernel_size=3, stride=1,\n","                                 padding=1, bias=False)\n","    self.bn2 = torch.nn.BatchNorm2d(out_channels)\n","    self.dropout2 = torch.nn.Dropout2d(self.dropout_prob)\n","    \n","    self.shortcut = torch.nn.Sequential()\n","    \n","    if stride != 1 or in_channels != out_channels:\n","      \n","      if option == 'A':\n","        self.shortcut = LambdaLayer(\n","            lambda x: torch.nn.functional.pad(\n","                x[:, :, ::2, ::2], \n","                (0, 0, 0, 0, out_channels//4, out_channels//4), \n","                'constant', 0)\n","                                   )\n","        \n","        \n","      elif option == 'B':\n","        self.shortcut = torch.nn.Sequential(\n","            torch.nn.Conv2d(in_channels, self.expansion * out_channels, \n","                            kernel_size=1, stride=stride, bias=False), \n","            torch.nn.BatchNorm2d(self.expansion * out_channels))\n","  \n","  def forward(self, x):\n","    out = self.conv1(x)\n","    if self.use_batch_norm:\n","      out = self.bn1(out)\n","    if self.use_dropout:\n","      out = self.dropout1(out)\n","    out = torch.nn.functional.relu(out)\n","      \n","    out = self.conv2(out)\n","    if self.use_batch_norm:\n","      out = self.bn2(out)\n","    if self.use_dropout:\n","      out = self.dropout2(out)\n","      \n","    out += self.shortcut(x)  \n","    out = torch.nn.functional.relu(out)\n","    \n","    return out\n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QuuJPNhQUfZY","colab_type":"code","colab":{}},"source":["def _weights_init(model):\n","  classname = model.__class__.__name__\n","  \n","  if isinstance(model, torch.nn.Linear) or isinstance(model, torch.nn.Conv2d):\n","    torch.nn.init.kaiming_normal_(model.weight)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vqmdMr-sNPAB","colab_type":"code","colab":{}},"source":["class ResNet(torch.nn.Module):\n","  def __init__(self,\n","               basic_block,\n","               list_num_blocks,\n","               use_batch_norm=True,\n","               use_dropout=False,\n","               dropout_prob=0.2,\n","               num_classes=10):\n","    super().__init__()\n","    self.use_batch_norm = use_batch_norm\n","    self.use_dropout = use_dropout\n","    self.dropout_prob = dropout_prob\n","    \n","    self.conv1 = torch.nn.Conv2d(3, 16, kernel_size=3, \n","                                 stride=1, padding=1, bias=False)\n","    self.bn1 = torch.nn.BatchNorm2d(16)\n","    self.dropout1 = torch.nn.Dropout2d(self.dropout_prob)\n","    \n","    self.in_channels = 16\n","    self.layer1 = self._make_layer(basic_block, 16, list_num_blocks[0], stride=1)\n","    self.layer2 = self._make_layer(basic_block, 32, list_num_blocks[1], stride=2)\n","    self.layer3 = self._make_layer(basic_block, 64, list_num_blocks[2], stride=2)\n","    \n","    self.fc1 = torch.nn.Linear(64, num_classes)\n","    \n","    self.apply(_weights_init)\n","    \n","  def _make_layer(self, basic_block, out_channels, num_blocks, stride):\n","    strides = [stride] + [1] * (num_blocks - 1)\n","    layers = []\n","    \n","    for stride in strides:\n","      layers.append(basic_block(self.in_channels, \n","                                out_channels, stride,\n","                                use_batch_norm=self.use_batch_norm,\n","                                use_dropout=self.use_dropout,\n","                                dropout_prob=self.dropout_prob))\n","      \n","      self.in_channels = out_channels * basic_block.expansion\n","      \n","    return torch.nn.Sequential(*layers)\n","  \n","  def forward(self, x):\n","    out = self.conv1(x)\n","    if self.use_batch_norm:\n","      out = self.bn1(out)\n","    if self.use_dropout:\n","      out = self.dropout1(out)\n","    \n","    out = torch.nn.functional.relu(out)\n","    out = self.layer1(out)\n","    out = self.layer2(out)\n","    out = self.layer3(out)\n","    out = torch.nn.functional.avg_pool2d(out, out.size()[3])\n","    \n","    out = out.view(out.size(0), -1)\n","    out = self.fc1(out)\n","    \n","    return out\n","      \n","    \n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"G4INaR-_WhP-","colab_type":"code","colab":{}},"source":["def resnet20(use_batch_norm=True, use_dropout=False, dropout_prob=0.2):\n","  return ResNet(BasicBlock, [3, 3, 3], \n","                use_batch_norm=use_batch_norm, \n","                use_dropout=use_dropout,\n","                dropout_prob=dropout_prob)\n","\n","def resnet110(use_batch_norm=True, use_dropout=False, dropout_prob=0.2):\n","  return ResNet(BasicBlock, [16, 20, 18],\n","                use_batch_norm=use_batch_norm, \n","                use_dropout=use_dropout,\n","                dropout_prob=dropout_prob)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5mu7La7GWzcG","colab_type":"code","colab":{}},"source":["def train(net, X_train, y_train, X_test, y_test):\n","  device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","  print(device)\n","  net = net.to(device)\n","  loss = torch.nn.CrossEntropyLoss()\n","  optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n","  \n","  batch_size = 100\n","  \n","  test_accuracy_history = []\n","  test_loss_history = []\n","  \n","  t = time.time()\n","  \n","#   X_test = X_test.to(device)\n","#   y_test = y_test.to(device)\n","  \n","  for epoch in range(30):\n","    order = np.random.permutation(len(X_train))\n","    \n","    for start_index in range(0, len(X_train), batch_size):\n","      optimizer.zero_grad()\n","      net.train()\n","      \n","      batch_indicies = order[start_index: start_index+batch_size]\n","      \n","      X_batch = X_train[batch_indicies].to(device)\n","      y_batch = y_train[batch_indicies].to(device)\n","      \n","      y_pred = net.forward(X_batch)\n","      \n","      loss_value = loss(y_pred, y_batch)\n","      loss_value.backward()\n","      \n","      optimizer.step()\n","      \n","    net.eval()\n","    with torch.no_grad():\n","      test_preds = net.forward(X_test.to(device))\n","      loss_val = loss(test_preds, y_test.to(device)).data.cpu()\n","      test_loss_history.append(loss_val)\n","\n","      accuracy = (test_preds.argmax(dim=1) == y_test.to(device)).float().mean().data.cpu()\n","      test_accuracy_history.append(accuracy)\n","      \n","      time_epoch = time.strftime(\"%H:%M:%S\", time.gmtime(time.time() - t))\n","      print('epoch:', epoch, ',', 'time:', time_epoch, ',', \n","            'accuracy:', accuracy, 'loss:', loss_val)\n","    \n","  print('learning is over')\n","  return test_accuracy_history, test_loss_history"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pArgzrvSDRIt","colab_type":"code","outputId":"3bf75d7f-21c3-48bb-ba18-6527158aad0a","executionInfo":{"status":"error","timestamp":1571128586506,"user_tz":-180,"elapsed":17263,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["accuracies, losses = {}, {}\n","\n","resnet18 = torchvision.models.resnet18()\n","\n","accuracies['resnet18'], losses['resnet18'] = train(resnet18, \n","                                                   X_train, y_train,\n","                                                   X_test, y_test)\n","\n","accuracies['resnet20_drop_0.2'], losses['resnet20_drop_0.2'] = train(resnet20(use_batch_norm=False, \n","                                                                              use_dropout=True,\n","                                                                              dropout_prob=0.2),\n","                                                                     X_train, y_train,\n","                                                                     X_test, y_test)\n","\n","accuracies['resnet20_drop_0.4'], losses['resnet20_drop_0.4'] = train(resnet20(use_batch_norm=False, \n","                                                                              use_dropout=True, \n","                                                                              dropout_prob=0.4),\n","                                                                     X_train, y_train,\n","                                                                     X_test, y_test)\n","\n","\n","accuracies['resnet20_bn_drop_0.2'], losses['resnet20_bn_drop_0.2'] = train(resnet20(use_batch_norm=True,\n","                                                                                    use_dropout=True, \n","                                                                                    dropout_prob=0.2),\n","                                                                           X_train, y_train,\n","                                                                           X_test, y_test)\n","\n","accuracies['resnet110_no_bn'], losses['resnet110_no_bn'] = train(resnet110(use_batch_norm=False, \n","                                                                           use_dropout=False, \n","                                                                           dropout_prob=0.2),\n","                                                                 X_train, y_train,\n","                                                                 X_test, y_test)\n","\n","accuracies['resnet110_bn_drop_0.2'], losses['resnet110_bn_drop_0.2'] = train(resnet110(use_batch_norm=True, \n","                                                                                       use_dropout=True, \n","                                                                                       dropout_prob=0.2),\n","                                                                             X_train, y_train,\n","                                                                             X_test, y_test)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["cuda:0\n","epoch: 0 , time: 00:00:39 , accuracy: tensor(0.5645) loss: tensor(1.2119)\n","epoch: 1 , time: 00:01:19 , accuracy: tensor(0.6580) loss: tensor(0.9822)\n","epoch: 2 , time: 00:01:58 , accuracy: tensor(0.6328) loss: tensor(1.0732)\n","epoch: 3 , time: 00:02:38 , accuracy: tensor(0.6961) loss: tensor(0.8809)\n","epoch: 4 , time: 00:03:17 , accuracy: tensor(0.7128) loss: tensor(0.8318)\n","epoch: 5 , time: 00:03:57 , accuracy: tensor(0.7052) loss: tensor(0.9034)\n","epoch: 6 , time: 00:04:37 , accuracy: tensor(0.7354) loss: tensor(0.8545)\n","epoch: 7 , time: 00:05:16 , accuracy: tensor(0.7470) loss: tensor(0.8359)\n","epoch: 8 , time: 00:05:56 , accuracy: tensor(0.7191) loss: tensor(0.9910)\n","epoch: 9 , time: 00:06:35 , accuracy: tensor(0.7551) loss: tensor(0.9097)\n","epoch: 10 , time: 00:07:15 , accuracy: tensor(0.7455) loss: tensor(0.9855)\n","epoch: 11 , time: 00:07:54 , accuracy: tensor(0.7452) loss: tensor(0.9932)\n","epoch: 12 , time: 00:08:34 , accuracy: tensor(0.7488) loss: tensor(1.0414)\n","epoch: 13 , time: 00:09:13 , accuracy: tensor(0.7265) loss: tensor(1.2475)\n","epoch: 14 , time: 00:09:52 , accuracy: tensor(0.7517) loss: tensor(1.0916)\n","epoch: 15 , time: 00:10:32 , accuracy: tensor(0.7386) loss: tensor(1.2248)\n","epoch: 16 , time: 00:11:11 , accuracy: tensor(0.7516) loss: tensor(1.1808)\n","epoch: 17 , time: 00:11:51 , accuracy: tensor(0.6880) loss: tensor(1.6254)\n","epoch: 18 , time: 00:12:31 , accuracy: tensor(0.7424) loss: tensor(1.3108)\n","epoch: 19 , time: 00:13:10 , accuracy: tensor(0.7096) loss: tensor(1.6514)\n","epoch: 20 , time: 00:13:50 , accuracy: tensor(0.7480) loss: tensor(1.2801)\n","epoch: 21 , time: 00:14:30 , accuracy: tensor(0.6922) loss: tensor(1.7773)\n","epoch: 22 , time: 00:15:09 , accuracy: tensor(0.7401) loss: tensor(1.3602)\n","epoch: 23 , time: 00:15:49 , accuracy: tensor(0.7512) loss: tensor(1.3534)\n","epoch: 24 , time: 00:16:29 , accuracy: tensor(0.7530) loss: tensor(1.3697)\n","epoch: 25 , time: 00:17:08 , accuracy: tensor(0.7487) loss: tensor(1.3689)\n","epoch: 26 , time: 00:17:48 , accuracy: tensor(0.7602) loss: tensor(1.2745)\n","epoch: 27 , time: 00:18:28 , accuracy: tensor(0.7408) loss: tensor(1.4344)\n","epoch: 28 , time: 00:19:08 , accuracy: tensor(0.7577) loss: tensor(1.3566)\n","epoch: 29 , time: 00:19:47 , accuracy: tensor(0.7589) loss: tensor(1.3427)\n","learning is over\n","cuda:0\n","epoch: 0 , time: 00:00:30 , accuracy: tensor(0.1344) loss: tensor(2.2417)\n","epoch: 1 , time: 00:01:01 , accuracy: tensor(0.3173) loss: tensor(1.9508)\n","epoch: 2 , time: 00:01:32 , accuracy: tensor(0.3962) loss: tensor(1.6990)\n","epoch: 3 , time: 00:02:03 , accuracy: tensor(0.4298) loss: tensor(1.5696)\n","epoch: 4 , time: 00:02:33 , accuracy: tensor(0.4531) loss: tensor(1.5136)\n","epoch: 5 , time: 00:03:04 , accuracy: tensor(0.4999) loss: tensor(1.4424)\n","epoch: 6 , time: 00:03:35 , accuracy: tensor(0.5145) loss: tensor(1.3880)\n","epoch: 7 , time: 00:04:05 , accuracy: tensor(0.5462) loss: tensor(1.3736)\n","epoch: 8 , time: 00:04:36 , accuracy: tensor(0.5744) loss: tensor(1.2398)\n","epoch: 9 , time: 00:05:07 , accuracy: tensor(0.5898) loss: tensor(1.1809)\n","epoch: 10 , time: 00:05:38 , accuracy: tensor(0.6115) loss: tensor(1.1532)\n","epoch: 11 , time: 00:06:08 , accuracy: tensor(0.6291) loss: tensor(1.0966)\n","epoch: 12 , time: 00:06:39 , accuracy: tensor(0.6433) loss: tensor(1.0482)\n","epoch: 13 , time: 00:07:10 , accuracy: tensor(0.6439) loss: tensor(1.0232)\n","epoch: 14 , time: 00:07:41 , accuracy: tensor(0.6717) loss: tensor(0.9934)\n","epoch: 15 , time: 00:08:11 , accuracy: tensor(0.6780) loss: tensor(0.9575)\n","epoch: 16 , time: 00:08:42 , accuracy: tensor(0.6805) loss: tensor(0.9451)\n","epoch: 17 , time: 00:09:12 , accuracy: tensor(0.6964) loss: tensor(0.9113)\n","epoch: 18 , time: 00:09:43 , accuracy: tensor(0.6978) loss: tensor(0.9065)\n","epoch: 19 , time: 00:10:14 , accuracy: tensor(0.7031) loss: tensor(0.9089)\n","epoch: 20 , time: 00:10:44 , accuracy: tensor(0.7122) loss: tensor(0.8592)\n","epoch: 21 , time: 00:11:15 , accuracy: tensor(0.7225) loss: tensor(0.8395)\n","epoch: 22 , time: 00:11:46 , accuracy: tensor(0.7160) loss: tensor(0.8545)\n","epoch: 23 , time: 00:12:16 , accuracy: tensor(0.7284) loss: tensor(0.8369)\n","epoch: 24 , time: 00:12:47 , accuracy: tensor(0.7244) loss: tensor(0.8477)\n","epoch: 25 , time: 00:13:18 , accuracy: tensor(0.7281) loss: tensor(0.8254)\n","epoch: 26 , time: 00:13:48 , accuracy: tensor(0.7318) loss: tensor(0.8161)\n","epoch: 27 , time: 00:14:19 , accuracy: tensor(0.7307) loss: tensor(0.8126)\n","epoch: 28 , time: 00:14:50 , accuracy: tensor(0.7407) loss: tensor(0.7873)\n","epoch: 29 , time: 00:15:20 , accuracy: tensor(0.7341) loss: tensor(0.8049)\n","learning is over\n","cuda:0\n","epoch: 0 , time: 00:00:30 , accuracy: tensor(0.1000) loss: tensor(2.3013)\n","epoch: 1 , time: 00:01:01 , accuracy: tensor(0.1607) loss: tensor(2.2746)\n","epoch: 2 , time: 00:01:31 , accuracy: tensor(0.1953) loss: tensor(2.2220)\n","epoch: 3 , time: 00:02:02 , accuracy: tensor(0.1889) loss: tensor(2.1653)\n","epoch: 4 , time: 00:02:32 , accuracy: tensor(0.2274) loss: tensor(2.1278)\n","epoch: 5 , time: 00:03:03 , accuracy: tensor(0.2634) loss: tensor(2.0173)\n","epoch: 6 , time: 00:03:33 , accuracy: tensor(0.2845) loss: tensor(1.9561)\n","epoch: 7 , time: 00:04:04 , accuracy: tensor(0.3321) loss: tensor(1.8503)\n","epoch: 8 , time: 00:04:34 , accuracy: tensor(0.3515) loss: tensor(1.8282)\n","epoch: 9 , time: 00:05:05 , accuracy: tensor(0.3570) loss: tensor(1.8254)\n","epoch: 10 , time: 00:05:35 , accuracy: tensor(0.3836) loss: tensor(1.7884)\n","epoch: 11 , time: 00:06:06 , accuracy: tensor(0.3832) loss: tensor(1.7803)\n","epoch: 12 , time: 00:06:36 , accuracy: tensor(0.4145) loss: tensor(1.6983)\n","epoch: 13 , time: 00:07:07 , accuracy: tensor(0.4319) loss: tensor(1.6367)\n","epoch: 14 , time: 00:07:37 , accuracy: tensor(0.4291) loss: tensor(1.6476)\n","epoch: 15 , time: 00:08:08 , accuracy: tensor(0.4261) loss: tensor(1.6498)\n","epoch: 16 , time: 00:08:39 , accuracy: tensor(0.4082) loss: tensor(1.6549)\n","epoch: 17 , time: 00:09:10 , accuracy: tensor(0.4172) loss: tensor(1.6340)\n","epoch: 18 , time: 00:09:40 , accuracy: tensor(0.4503) loss: tensor(1.5834)\n","epoch: 19 , time: 00:10:11 , accuracy: tensor(0.4476) loss: tensor(1.5625)\n","epoch: 20 , time: 00:10:42 , accuracy: tensor(0.4498) loss: tensor(1.5572)\n","epoch: 21 , time: 00:11:13 , accuracy: tensor(0.4500) loss: tensor(1.5706)\n","epoch: 22 , time: 00:11:43 , accuracy: tensor(0.4465) loss: tensor(1.5906)\n","epoch: 23 , time: 00:12:14 , accuracy: tensor(0.4342) loss: tensor(1.5649)\n","epoch: 24 , time: 00:12:45 , accuracy: tensor(0.4456) loss: tensor(1.5779)\n","epoch: 25 , time: 00:13:15 , accuracy: tensor(0.4652) loss: tensor(1.5501)\n","epoch: 26 , time: 00:13:46 , accuracy: tensor(0.4713) loss: tensor(1.5078)\n","epoch: 27 , time: 00:14:17 , accuracy: tensor(0.4716) loss: tensor(1.5205)\n","epoch: 28 , time: 00:14:48 , accuracy: tensor(0.4733) loss: tensor(1.5076)\n","epoch: 29 , time: 00:15:18 , accuracy: tensor(0.4645) loss: tensor(1.4694)\n","learning is over\n","cuda:0\n","epoch: 0 , time: 00:00:34 , accuracy: tensor(0.4034) loss: tensor(1.6462)\n","epoch: 1 , time: 00:01:09 , accuracy: tensor(0.4558) loss: tensor(1.4939)\n","epoch: 2 , time: 00:01:43 , accuracy: tensor(0.5080) loss: tensor(1.3311)\n","epoch: 3 , time: 00:02:18 , accuracy: tensor(0.5450) loss: tensor(1.2578)\n","epoch: 4 , time: 00:02:53 , accuracy: tensor(0.5842) loss: tensor(1.1453)\n","epoch: 5 , time: 00:03:27 , accuracy: tensor(0.6201) loss: tensor(1.0568)\n","epoch: 6 , time: 00:04:02 , accuracy: tensor(0.6448) loss: tensor(1.0074)\n","epoch: 7 , time: 00:04:36 , accuracy: tensor(0.6615) loss: tensor(0.9437)\n","epoch: 8 , time: 00:05:11 , accuracy: tensor(0.6808) loss: tensor(0.8896)\n","epoch: 9 , time: 00:05:46 , accuracy: tensor(0.7114) loss: tensor(0.8197)\n","epoch: 10 , time: 00:06:20 , accuracy: tensor(0.7239) loss: tensor(0.7753)\n","epoch: 11 , time: 00:06:55 , accuracy: tensor(0.7340) loss: tensor(0.7619)\n","epoch: 12 , time: 00:07:30 , accuracy: tensor(0.7473) loss: tensor(0.7213)\n","epoch: 13 , time: 00:08:05 , accuracy: tensor(0.7596) loss: tensor(0.6848)\n","epoch: 14 , time: 00:08:39 , accuracy: tensor(0.7655) loss: tensor(0.6740)\n","epoch: 15 , time: 00:09:14 , accuracy: tensor(0.7709) loss: tensor(0.6667)\n","epoch: 16 , time: 00:09:49 , accuracy: tensor(0.7784) loss: tensor(0.6400)\n","epoch: 17 , time: 00:10:24 , accuracy: tensor(0.7851) loss: tensor(0.6241)\n","epoch: 18 , time: 00:10:59 , accuracy: tensor(0.7896) loss: tensor(0.6033)\n","epoch: 19 , time: 00:11:34 , accuracy: tensor(0.7948) loss: tensor(0.5988)\n","epoch: 20 , time: 00:12:09 , accuracy: tensor(0.8012) loss: tensor(0.5826)\n","epoch: 21 , time: 00:12:44 , accuracy: tensor(0.7995) loss: tensor(0.5812)\n","epoch: 22 , time: 00:13:19 , accuracy: tensor(0.8038) loss: tensor(0.5697)\n","epoch: 23 , time: 00:13:53 , accuracy: tensor(0.7983) loss: tensor(0.5880)\n","epoch: 24 , time: 00:14:28 , accuracy: tensor(0.8126) loss: tensor(0.5540)\n","epoch: 25 , time: 00:15:03 , accuracy: tensor(0.8072) loss: tensor(0.5615)\n","epoch: 26 , time: 00:15:37 , accuracy: tensor(0.8144) loss: tensor(0.5461)\n","epoch: 27 , time: 00:16:12 , accuracy: tensor(0.8123) loss: tensor(0.5443)\n","epoch: 28 , time: 00:16:46 , accuracy: tensor(0.8228) loss: tensor(0.5235)\n","epoch: 29 , time: 00:17:21 , accuracy: tensor(0.8215) loss: tensor(0.5348)\n","learning is over\n","cuda:0\n","epoch: 0 , time: 00:02:48 , accuracy: tensor(0.1219) loss: tensor(2.3003)\n","epoch: 1 , time: 00:05:37 , accuracy: tensor(0.1697) loss: tensor(2.1925)\n","epoch: 2 , time: 00:08:25 , accuracy: tensor(0.1826) loss: tensor(2.1407)\n","epoch: 3 , time: 00:11:14 , accuracy: tensor(0.1927) loss: tensor(2.0963)\n","epoch: 4 , time: 00:14:03 , accuracy: tensor(0.2185) loss: tensor(2.0583)\n","epoch: 5 , time: 00:16:52 , accuracy: tensor(0.2367) loss: tensor(2.0284)\n","epoch: 6 , time: 00:19:41 , accuracy: tensor(0.2473) loss: tensor(2.0007)\n","epoch: 7 , time: 00:22:30 , accuracy: tensor(0.2568) loss: tensor(1.9938)\n","epoch: 8 , time: 00:25:19 , accuracy: tensor(0.2773) loss: tensor(1.9402)\n","epoch: 9 , time: 00:28:08 , accuracy: tensor(0.2807) loss: tensor(1.9300)\n","epoch: 10 , time: 00:30:57 , accuracy: tensor(0.2787) loss: tensor(1.9381)\n","epoch: 11 , time: 00:33:46 , accuracy: tensor(0.3209) loss: tensor(1.8460)\n","epoch: 12 , time: 00:36:35 , accuracy: tensor(0.3055) loss: tensor(1.9271)\n","epoch: 13 , time: 00:39:24 , accuracy: tensor(0.3349) loss: tensor(1.7999)\n","epoch: 14 , time: 00:42:13 , accuracy: tensor(0.3454) loss: tensor(1.7643)\n","epoch: 15 , time: 00:45:02 , accuracy: tensor(0.3190) loss: tensor(1.8803)\n","epoch: 16 , time: 00:47:51 , accuracy: tensor(0.3565) loss: tensor(1.7675)\n","epoch: 17 , time: 00:50:40 , accuracy: tensor(0.3651) loss: tensor(1.7519)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"YWtmqHdvcwWA","colab_type":"code","colab":{}},"source":["def exp_plotter(data, name):\n","  plt.figure(figsize=(10, 6))\n","  for exp_id in data.keys():\n","    plt.plot(data[exp_id], label=exp_id)\n","  plt.legend()\n","  plt.title(str(name))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dtn0BfTdjDDE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":180},"outputId":"dd94a835-5fbc-4fe4-caa5-816e3c156556","executionInfo":{"status":"error","timestamp":1571171738899,"user_tz":-180,"elapsed":1361,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}}},"source":["exp_plotter(accuracies, 'accuracies')\n","exp_plotter(losses, 'losses')"],"execution_count":2,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-e4a70eb85dc5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexp_plotter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'accuracies'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mexp_plotter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'losses'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'accuracies' is not defined"]}]},{"cell_type":"code","metadata":{"id":"LKWjjrYSjLqY","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}